Abstract

Common generic segmentation methods are obstructed by sudden changes in illumination. Significant increase of brightness by light switching on and shadows cast by objects often cause these methods to produce erroneous classifications. To enable illumination-invariant segmentation, the Collinear Vector Model discussed in this thesis constructs RGB color vectors from local pixel neighborhoods. Variations in brightness only influence the length of these vectors by a scalar value. Therefore an orthogonal distance measure can be employed to determine the local color similarity under illumination invariance. In the presence of additive noise, the vector collinearity is estimated by finding the minimum orthogonal distances from the vectors to the unknown noise-free signal. The distance minimization can be defined as a smallest eigenvalue problem. This minimum is incorporated into a Bayesian framework, which allows for maximization of the a-posteriori probability (MAP) of the decision. The resulting value is compared against a static and an adaptive threshold. The classffication labels are considered to be sampled by a Markov random field (MRF) to model the pixel interdependencies. The corresponding energy function is defined as the integration of the evidence over a spatial neighborhood. This induces spatial compactness and smooth edges in the foreground mask. Performance is measured using both the PETS 2001 dataset and a specific illumination test set. The Collinear Vector Model is used in an interactive video art application. Therefore, the second focus of this work discusses the academic interest in art. One of the many purposes of art is to attract and stimulate the curiosity of people. To this end a video distorting mirror has been devised that produces time-delayed 'floating' copies of objects in movement. The Collinear Vector Model replaces static parts of the scene to emphasize this motion. The proposed Pool of Intentions algorithm will be deployed as part of the Science LinX program of the University of Groningen to express the creativity and transparency of academic research.